shape <- readOGR('./TreesCityBoulder', layer='TreesCityBoulder')
## OGR data source with driver: ESRI Shapefile
## Source: "./TreesCityBoulder", layer: "TreesCityBoulder"
## with 41839 features
## It has 38 fields
shape_latlon <- spTransform(shape, CRS("+proj=longlat +datum=WGS84"))
head(shape_latlon)
## ID UNIQUEID ADDRESS SUFFIX STREET ONSTR
## 1 59084 JS 20150203131235 601 <NA> 10TH ST 10TH ST
## 2 59085 JS 20150203131431 601 <NA> 10TH ST 10TH ST
## 3 86480 DROID 201607141607903829845 601 <NA> 10TH ST 10TH ST
## 4 59087 JS 20150203131448 601 <NA> 10TH ST 10TH ST
## 5 59088 JS 20150203131959 690 <NA> 10TH ST 10TH ST
## 6 59089 JS 20150203132037 690 <NA> 10TH ST 10TH ST
## FROMSTR TOSTR SIDE SITE SPP DBH CULTIVAR
## 1 BASELINE RD CUL-DE-SAC Front 1 Picea pungens 5 <NA>
## 2 BASELINE RD CUL-DE-SAC Front 2 Picea pungens 2 <NA>
## 3 BASELINE RD CUL-DE-SAC Front 3 Ulmus pumila 6 <NA>
## 4 BASELINE RD CUL-DE-SAC Front 4 Acer platanoides 6 <NA>
## 5 CUL-DE-SAC BASELINE RD Front 1 Acer platanoides 7 <NA>
## 6 CUL-DE-SAC BASELINE RD Front 2 Acer platanoides 3 <NA>
## CONDITION INSPECT MT MT2 TTYPE MEMTREE
## 1 Excellent N No Specific Maintenance Need Routine Evergreen No
## 2 Good N No Specific Maintenance Need Routine Evergreen No
## 3 Dead N No Specific Maintenance Need Routine Deciduous No
## 4 Fair N No Specific Maintenance Need Routine Deciduous No
## 5 Fair N No Specific Maintenance Need Routine Deciduous No
## 6 Good N No Specific Maintenance Need Routine Deciduous No
## DISEASEDEF STRUTDEFEC LOCTYPE SITECAT HOOD
## 1 N/A N/A Street Shrub /Mulch bed BSLO - Baseline South
## 2 N/A N/A Street Shrub /Mulch bed BSLO - Baseline South
## 3 N/A trunk wounds Street Shrub /Mulch bed BSLO - Baseline South
## 4 N/A trunk wounds Street Shrub /Mulch bed BSLO - Baseline South
## 5 N/A deadwood Street Bare dirt BSLO - Baseline South
## 6 N/A N/A Street Bare dirt BSLO - Baseline South
## LOCATION JURISDIC TSIP GRATESZ GUARDSZ EAB INV_DATE
## 1 Street Forestry N Unassigned Unassigned Unassigned 2015/02/03
## 2 Street Forestry N Unassigned Unassigned Unassigned 2015/02/03
## 3 Street Forestry N Unassigned Unassigned Unassigned 2016/07/14
## 4 Street Forestry N Unassigned Unassigned Unassigned 2015/02/03
## 5 Street Forestry N Unassigned Unassigned Unassigned 2015/02/03
## 6 Street Forestry N Unassigned Unassigned Unassigned 2015/02/03
## INV_TIME INSPECT_DT INSPECT_TM NOTES ACTIVE COMMONNAME GENUS
## 1 <NA> 2015/07/10 11:40:29 <NA> 1 Blue Spruce Picea
## 2 <NA> 2015/07/10 11:40:29 <NA> 1 Blue Spruce Picea
## 3 <NA> 2017/03/09 12:46:32 TC np. 1 Siberian Elm Ulmus
## 4 <NA> 2015/07/10 11:40:30 <NA> 1 Norway Maple Acer
## 5 <NA> 2015/07/10 11:40:30 <NA> 1 Norway Maple Acer
## 6 <NA> 2015/07/10 11:40:30 cluster 1 Norway Maple Acer
csv <- read.csv('Trees_Public.csv')
head(csv)
## FID ID UNIQUEID ADDRESS SUFFIX STREET ONSTR
## 1 0 59084 JS 20150203131235 601 10TH ST 10TH ST
## 2 1 59085 JS 20150203131431 601 10TH ST 10TH ST
## 3 2 86480 DROID 201607141607903829845 601 10TH ST 10TH ST
## 4 3 59087 JS 20150203131448 601 10TH ST 10TH ST
## 5 4 59088 JS 20150203131959 690 10TH ST 10TH ST
## 6 5 59089 JS 20150203132037 690 10TH ST 10TH ST
## FROMSTR TOSTR SIDE SITE SPP DBH CULTIVAR
## 1 BASELINE RD CUL-DE-SAC Front 1 Picea pungens 5
## 2 BASELINE RD CUL-DE-SAC Front 2 Picea pungens 2
## 3 BASELINE RD CUL-DE-SAC Front 3 Ulmus pumila 6
## 4 BASELINE RD CUL-DE-SAC Front 4 Acer platanoides 6
## 5 CUL-DE-SAC BASELINE RD Front 1 Acer platanoides 7
## 6 CUL-DE-SAC BASELINE RD Front 2 Acer platanoides 3
## CONDITION INSPECT MT MT2 TTYPE MEMTREE
## 1 Excellent N No Specific Maintenance Need Routine Evergreen No
## 2 Good N No Specific Maintenance Need Routine Evergreen No
## 3 Dead N No Specific Maintenance Need Routine Deciduous No
## 4 Fair N No Specific Maintenance Need Routine Deciduous No
## 5 Fair N No Specific Maintenance Need Routine Deciduous No
## 6 Good N No Specific Maintenance Need Routine Deciduous No
## DISEASEDEF STRUTDEFEC LOCTYPE SITECAT HOOD
## 1 N/A N/A Street Shrub /Mulch bed BSLO - Baseline South
## 2 N/A N/A Street Shrub /Mulch bed BSLO - Baseline South
## 3 N/A trunk wounds Street Shrub /Mulch bed BSLO - Baseline South
## 4 N/A trunk wounds Street Shrub /Mulch bed BSLO - Baseline South
## 5 N/A deadwood Street Bare dirt BSLO - Baseline South
## 6 N/A N/A Street Bare dirt BSLO - Baseline South
## LOCATION JURISDIC TSIP GRATESZ GUARDSZ EAB
## 1 Street Forestry N Unassigned Unassigned Unassigned
## 2 Street Forestry N Unassigned Unassigned Unassigned
## 3 Street Forestry N Unassigned Unassigned Unassigned
## 4 Street Forestry N Unassigned Unassigned Unassigned
## 5 Street Forestry N Unassigned Unassigned Unassigned
## 6 Street Forestry N Unassigned Unassigned Unassigned
## INV_DATE INV_TIME INSPECT_DT INSPECT_TM NOTES
## 1 2015-02-03 00:00:00 NA 2015-07-10 00:00:00 11:40:29
## 2 2015-02-03 00:00:00 NA 2015-07-10 00:00:00 11:40:29
## 3 2016-07-14 00:00:00 NA 2017-03-09 00:00:00 12:46:32 TC np.
## 4 2015-02-03 00:00:00 NA 2015-07-10 00:00:00 11:40:30
## 5 2015-02-03 00:00:00 NA 2015-07-10 00:00:00 11:40:30
## 6 2015-02-03 00:00:00 NA 2015-07-10 00:00:00 11:40:30 cluster
## ACTIVE COMMONNAME GENUS
## 1 1 Blue Spruce Picea
## 2 1 Blue Spruce Picea
## 3 1 Siberian Elm Ulmus
## 4 1 Norway Maple Acer
## 5 1 Norway Maple Acer
## 6 1 Norway Maple Acer
summary(csv$EAB)
## Group A Group B Group C Private Treatment
## 226 565 534 127
## Removal Unassigned
## 41 49176
#ggplot(data=csv) + geom_bar(mapping = aes(x = GENUS))
filtered <- shape_latlon[shape_latlon$EAB != 'Unassigned',]
pal = colorFactor(palette = "RdYlBu", domain=filtered$EAB)
leaflet(data=filtered) %>% addProviderTiles(providers$CartoDB.Positron) %>%
addCircles(color=~pal(filtered$EAB)) %>% addLegend("bottomright", pal=pal, filtered$EAB)
filtered <- shape_latlon[shape_latlon$COMMONNAME == "Green Ash" | shape_latlon$COMMONNAME == "White Ash",]
pal = colorFactor(palette = "RdYlBu", domain=filtered$COMMONNAME)
leaflet(data=filtered) %>% addProviderTiles(providers$CartoDB.Positron) %>%
addCircles(color=~pal(filtered$COMMONNAME)) %>% addLegend("bottomright", pal=pal, filtered$COMMONNAME)
## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette RdYlBu is 11
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette RdYlBu is 11
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette RdYlBu is 11
## Returning the palette you asked for with that many colors
filtered <- shape_latlon[shape_latlon$COMMONNAME == "Green Ash" | shape_latlon$COMMONNAME == "White Ash",]
pal = colorFactor(palette = "RdYlBu", domain=c("Dead","Very Poor", "Poor", "Fair", "Good", "Excellent", "N/A"))
leaflet(data=filtered) %>% addProviderTiles(providers$CartoDB.Positron) %>%
addCircles(color=~pal(filtered$CONDITION)) %>% addLegend("bottomright", pal=pal, filtered$CONDITION)
library(leaflet.extras)
## Warning: package 'leaflet.extras' was built under R version 3.4.4
shape_latlon$is_ash <- shape_latlon$COMMONNAME == "Green Ash" | shape_latlon$COMMONNAME == "White Ash"
pal = colorFactor(palette = topo.colors(2), domain=c("TRUE", "FALSE"))
leaflet(data=filtered) %>% addProviderTiles(providers$CartoDB.Positron) %>% addHeatmap(radius=6)
#addCircles(color=~pal(shape_latlon$is_ash)) %>%
#addLegend("bottomright", pal=pal, shape_latlon$is_ash)
subdivisions <- readOGR('./ACS1216_bg', layer='ACS1216_bg')
## OGR data source with driver: ESRI Shapefile
## Source: "./ACS1216_bg", layer: "ACS1216_bg"
## with 3532 features
## It has 257 fields
subdivisions_latlong <- spTransform(subdivisions, CRS("+proj=longlat +datum=WGS84"))
citylims <- readOGR('./Boulder_City_Limits', layer='Boulder_City_Limits')
## OGR data source with driver: ESRI Shapefile
## Source: "./Boulder_City_Limits", layer: "Boulder_City_Limits"
## with 5 features
## It has 3 fields
## Integer64 fields read as strings: OBJECTID
citylims_latlong <- spTransform(citylims, CRS("+proj=longlat +datum=WGS84"))
#leaflet(data=citylims_latlong) %>% addProviderTiles(providers$CartoDB.Positron) %>% addPolygons()
filtered_blocks <- crop(subdivisions_latlong, citylims_latlong)
blocks_objectid <- filtered_blocks[c("OBJECTID")]
block_ref <- intersect(shape_latlon, blocks_objectid)
summary(block_ref$d)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 105 1363 2695 2094 2897 3491
pal = colorFactor(palette = "RdYlBu", domain=block_ref$d)
leaflet(data=block_ref[block_ref$COMMONNAME == "White Ash" | block_ref$COMMONNAME == "Green Ash",]) %>% addProviderTiles(providers$CartoDB.Positron) %>% addPolygons(data=filtered_blocks) %>% addCircles(data = block_ref, color = ~pal(block_ref$d))
## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette RdYlBu is 11
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette RdYlBu is 11
## Returning the palette you asked for with that many colors
#data.frame(block_ref)
block_gb <- data.frame(block_ref) %>% group_by(d) %>% tally()
block_gb_ash <- data.frame(block_ref[block_ref$COMMONNAME == "White Ash" | block_ref$COMMONNAME == "Green Ash",]) %>% group_by(d) %>% tally()
block_gb_merge <- merge(block_gb, block_gb_ash, by="d")
block_gb_merge$ratio <- block_gb_merge$n.y/block_gb_merge$n.x
qplot(block_gb_merge$ratio, geom="histogram", bins=12)
block_gb_merge$zscore <- (block_gb_merge$ratio-mean(block_gb_merge$ratio))/sd(block_gb_merge$ratio)
sp_blocks_zscore <- merge(blocks_objectid, block_gb_merge, by.x="OBJECTID", by.y="d")
colors <- colorBin("PRGn", domain = sp_blocks_zscore$zscore, bins = c(-3,-2,-1,0,1,2,3))
leaflet(sp_blocks_zscore) %>% addProviderTiles(providers$CartoDB.Positron) %>% addPolygons(color=colors(sp_blocks_zscore$zscore), weight=3, fillOpacity = .7) %>% addLegend("bottomright",
pal = colors,
values = sp_blocks_zscore$zscore,
title = "Z-score of % of ash trees")# %>% addHeatmap(data=filtered, radius=6)
## Warning in colors(sp_blocks_zscore$zscore): Some values were outside the
## color scale and will be treated as NA